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One can readily see that, in a given year, the last day of February and March 1 are a good test dates. As an aside note, if we have a three-digit number abc, where a, b, and c are the digits, each nonpositive if abc is nonpositive; we have (abc) mod 7 = 9*a + 3*b + c. Repeat the formula down to a single digit.
Pages in category "Articles with example MATLAB/Octave code" The following 40 pages are in this category, out of 40 total. This list may not reflect recent changes. A.
For determination of the day of the week (1 January 2000, Saturday) the day of the month: 1 ~ 31 (1) the month: (6) the year: (0) the century mod 4 for the Gregorian calendar and mod 7 for the Julian calendar (0). adding 1+6+0+0=7. Dividing by 7 leaves a remainder of 0, so the day of the week is Saturday. The formula is w = (d + m + y + c) mod 7.
Since in the Gregorian calendar there are 146,097 days, or exactly 20,871 seven-day weeks, in 400 years, the anchor day repeats every four centuries. For example, the anchor day of 1700–1799 is the same as the anchor day of 2100–2199, i.e. Sunday. The full 400-year cycle of doomsdays is given in the adjacent table.
[a] For example, the Julian day number for the day starting at 12:00 UT (noon) on January 1, 2000, was 2 451 545. [ 7 ] The Julian date ( JD ) of any instant is the Julian day number plus the fraction of a day since the preceding noon in Universal Time.
If the values instead were a random sample drawn from some large parent population (for example, there were 8 students randomly and independently chosen from a class of 2 million), then one divides by 7 (which is n − 1) instead of 8 (which is n) in the denominator of the last formula, and the result is = /
The use of n − 1 instead of n in the formula for the sample variance is known as Bessel's correction, which corrects the bias in the estimation of the population variance, and some, but not all of the bias in the estimation of the population standard deviation.
A probability distribution is not uniquely determined by the moments E[X n] = e nμ + 1 / 2 n 2 σ 2 for n ≥ 1. That is, there exist other distributions with the same set of moments. [ 4 ] In fact, there is a whole family of distributions with the same moments as the log-normal distribution.